- Home
-
Board
- Editorial Board
- Apply For Reviewer
-
Research Publishing Standards
- Guidelines
- Peer Review Policy
- Short Guide to New Editors
- Retraction Guidelines
- Research Institute
- Recommendations for the Board of Directors
- Editor Research Audit and Service Assessments
- Guidelines for Journals Publishers
- Code of Conduct for Journal Publishers
- Commandment in Social Media
- Suggestions
- Authorship Disputes
- Rights and Responsibilities
- Editors Home
- Visibility
- Information
- Apply for Editorial
- About
- Call for Paper
- Research Community
-
Journals
- Computer Science
-
Medical Research
- A: Neurology & Nervous System
- B: Pharma, Drug Discovery, Toxicology & Medicine
- C: Microbiology & Pathology
- D: Radiology, Diagnostic Imaging and Instrumentation
- E: Gynecology & Obstetrics
- F: Diseasescancer, ophthalmology & pediatric
- G: Veterinary Science & Veterinary Medicine
- H: Orthopedic & Musculoskeletal System
- I: Surgeries & Cardiovascular System
- J: Dentistry & Otolaryngology
- K: Interdisciplinary
- L:Nutrition and Food Science
- Engineering
- Science Frontier Research
- Management
-
Human-Social Science
- A: Arts & Humanities psychology, public administration, library sciences, sports, arts, media, music
- B: Geography, Environmental Science & Disaster Management
- C: Sociology & Culture
- D: History, Archaeology & Anthropology
- E: Economics
- F: Political Science
- G: Linguistics & Education
- H: Interdisciplinary
-
Fellow
-
Support
Global Journals, MA, US×
Hello, how can we assist you?
Recognition of Cursive Arabic Handwritten Text using Embedded Training based on HMMs
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.
